The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Recognizing observed activities, such as an activity represented in video data, is a very complex undertaking. Known techniques have failed to gain market penetration, especially with respect to consumer interactions. Some effort has been directed toward identifying a user's activities with respect to web interactions, as exemplified by U.S. patent application publication 2014/0149418 to Qin et al. titled “Method and System for Measuring Social Influence and Receptivity of Users”, filed Nov. 28, 2012. However, the techniques disclosed by Qin are not applicable to recognizing observed activities.
Other efforts have focused on using directed graphs for activity recognition. Examples of such techniques are described in the following papers:    “Graph Degree Linkage: Agglomerative Clustering on a Directed Graph”, by Zhang et al., Proceedings of European Conference on Computer Vision (ECCV), 2012 (“Zhang 2012”);    “Action Recognition by Dense Trajectories”, by Wang et al., CVPR 2011—IEEE Conference on Computer Vision & Pattern Recognition (2011) 3169-3176 (“Wang 2011”); and    “Directed Acyclic Graph Kernels for Action Recognition”, by Wang et al., 2013 IEEE International Conference on Computer Vision (ICCV) (“Wang 2013”).
The above approaches do relate to recognizing observed activities through the use of specialized graphs. For example, Wang 2013 provides a foundation for recognizing activities through the use of directed acyclic graphs (DAGs). However, the computation time necessary for such an approach is prohibitive for use in consumer device applications, such as for use in cameras, cell phones, tablet computers, toys, vehicles and other consumer devices.